In the following section we introduce the back-propagation learning algorithm and use mini-batching to express the most compute intensive equations in matrix form. Once expressed in matrix form, hardware optimized numerical linear algebra routines are used to achieve an efficient mapping of the algorithm on to the Intel Xeon Phi co-processor. Section 3 describes the preparation of the data used to train the DNN. Section 4 describes the implementation of the deep neural networks. Section 5 then presents results measuring the performance of a DNN. Finally in Section 6, we demonstrate the application of DNNs to backtesting using a walk forward methodology, and provide performance results for a simple buy-hold-sell strategy.